add indoor sample test unit

mmdet3d/datasets/pipelines/indoor_sample.py

@@ -7,13 +7,19 @@ def points_random_sampling(points,
                            num_samples,
                            replace=None,
                            return_choices=False):
-    """Points Random Sampling
+    """Points Random Sampling.
 
     Sample points to a certain number.
 
     Args:
         points (ndarray): 3D Points.
         num_samples (int): Number of samples to be sampled.
+        replace (bool): Whether the sample is with or without replacement.
+        return_choices (bool): Whether return choice.
+
+    Returns:
+        points (ndarray): 3D Points.
+        choices (ndarray): The generated random samples
     """
     if replace is None:
         replace = (points.shape[0] < num_samples)
@@ -25,8 +31,8 @@ def points_random_sampling(points,
 
 
 @PIPELINES.register_module
-class IndoorSamplePoints(object):
-    """Indoor Sample Points
+class PointSample(object):
+    """Point Sample.
 
     Sampling data to a certain number.
 
@@ -61,5 +67,6 @@ class IndoorSamplePoints(object):
 
     def __repr__(self):
         repr_str = self.__class__.__name__
+        repr_str += '(dataset_name={})'.format(self.name)
         repr_str += '(num_points={})'.format(self.num_points)
         return repr_str

tests/test_indoor_sample.py

+import numpy as np
+
+from mmdet3d.datasets.pipelines.indoor_sample import PointSample
+
+
+def test_indoor_sample():
+    scannet_sample_points = PointSample('scannet', 1)
+    scannet_results = dict()
+    scannet_results['points'] = np.array(
+        [[1.6110241e+00, -1.6903955e-01, 5.8115810e-01, 5.9897250e-01],
+         [1.3978075e+00, 4.2035791e-01, 3.8729519e-01, 4.0510958e-01]])
+    scannet_results['instance_labels'] = np.array([9, 3])
+    scannet_results['pcl_color'] = np.array([[29.0, 142.0, 122.0],
+                                             [21., 17., 16.]])
+    scannet_results['semantic_labels'] = np.array([1, 5])
+
+    scannet_results = scannet_sample_points(scannet_results)
+    points = scannet_results.get('points', None)
+    pcl_color = scannet_results.get('pcl_color', None)
+    instance_labels = scannet_results.get('instance_labels', None)
+    semantic_labels = scannet_results.get('semantic_labels', None)
+    assert points.shape == (1, 4)
+    assert pcl_color.shape == (1, 3)
+    assert instance_labels.shape == (1, )
+    assert semantic_labels.shape == (1, )
+
+    sunrgbd_sample_points = PointSample('sunrgbd', 1)
+    sunrgbd_results = dict()
+    sunrgbd_results['points'] = np.array(
+        [[1.2113925, 2.8755326, -1.1801991, 0.01056887],
+         [3.6554186, 4.5093756, 0.33279705, 1.523565]])
+
+    sunrgbd_results = sunrgbd_sample_points(sunrgbd_results)
+    point_cloud = sunrgbd_results.get('points', None)
+    assert point_cloud.shape == (1, 4)
+
+
+test_indoor_sample()